code: def __init__(
      self,
      agent_parameters,
      parent: Union["LevelManager", "CompositeAgent"] = None,
  ):
      super().__init__(agent_parameters, parent)
      # signals definition
      self.q_loss = self.register_signal("Q Loss")
      self.policy_loss = self.register_signal("Policy Loss")
      self.probability_loss = self.register_signal("Probability Loss")
      self.bias_correction_loss = self.register_signal(
          "Bias Correction Loss"
      )
      self.unclipped_grads = self.register_signal("Grads (unclipped)")
      self.V_Values = self.register_signal("Values")
      self.kl_divergence = self.register_signal("KL Divergence")
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import ACERPolicyHeadParameters, QHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters
  from rl_coach.core_types import Batch
  from rl_coach.exploration_policies.categorical import CategoricalParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.spaces import DiscreteActionSpace
  from rl_coach.utils import eps, last_sample
label_desc: Use image node_id 1 to create a new ACERAgent object from inherited base classes: PolicyOptimizationAgent with example: obj = ACERAgent(agent_parameters, parent)